The advent of Advanced Driver Assistance Systems (ADAS) and autonomous driving has offered a new challenge for functional verification and validation. The explosion of the test sample space for possible combinations of inputs needs to be handled in an intelligent manner to meet cost and time targets for the development of such systems. Various test methods like VEHiL (Vehicle Hardware-in-the-Loop), Vehicle-in-the-Loop and Co-ordinated automated driving have been developed for validation of ADAS and autonomous systems. Increasingly, driving simulators are being used for testing ADAS and autonomous systems as they offer a safer and a more reproducible environment for verifying such systems. While each of these test methods serves a specific purpose, they have a common challenge between them. All of these methods require the generation of test scenarios for which the systems are to be tested. This paper addresses this research gap by using constrained randomization techniques for creation of the required test scenarios. Furthermore, this paper proposes an automated constrained randomized test scenario generation for testing of ADAS and autonomous systems in a driving simulator setup. The constrained randomization is deployed at two levels: 1) static randomization 2) dynamic randomization. Static randomization is used to automatically create the base scenario w.r.t., possible path trajectories of vehicles, environment variables and traffic variables. Dynamic randomization is achieved by real-time communication between the driving simulator and the constrained randomization tool via a TCP/IP HiL interface (client-server interface). Constrained randomization is then used to intelligently explore the possible sample space to find the corner cases for which an ADAS or an autonomous system may fail. The test setup comprises of the 3xD Simulator for Intelligent Vehicles at WMG, University of Warwick, UK which is integrated with the Vitaq tool, which is a constrained randomization test automation tool developed by Vertizan Limited.